Two major research needs emerge from the literature on understanding childhood obesity and on designing effective interventions to address this major threat to public health. The first is a need for a joint understanding of biological and social determinants and their interactions. The second is a need to develop a better understanding of childhood obesity as a longitudinal trajectory of weight status. To meet those needs this study will develop methods that combine two longitudinal survey datasets to estimate and simulate trajectories of weight status from birth to adolescence. Modeling multiple (more than three) levels of biological and social influence simultaneously is beyond the feasibility of current methods and data. A single survey may have some, but not all of the variables need to estimate these effects, nor will a single survey have sufficient sample size for estimation of these multiple effects in a longitudinal model. Our solution to the problem of how to include more than three levels of influence involves: (1) the use of existing statistical modeling methods for up to two or three levels of influence in any one equation; (2) the use of multiple imputation from one survey to another to allow pooling of child observations across two surveys to greatly increase effective sample sizes in this model estimation; and (3) the use of Markov-chain simulation models to allow for more than three levels of influences to be accumulated across four separate equations describing the childhood weight-status trajectory. Estimates of the absolute and relative impacts on childhood obesity of biological, behavioral, and socio-economic variables, and of potential policy interventions, are then derived from simulated childhood life paths. PUBLIC HEALTH RELEVANCE: In this study, we develop statistical and computational methods that combine two longitudinal survey datasets to estimate and simulate trajectories of weight status from birth to adolescence. Estimates of the impacts on childhood obesity of biological, behavioral, and socio-economic variables, and of potential policy interventions, are derived from simulated childhood life paths.